{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "from time import time\n",
    "from PIL import Image\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import *\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import accuracy_score\n",
    "import networkx as nx\n",
    "import random\n",
    "\n",
    "from keras import layers, models, optimizers\n",
    "from keras import backend as K\n",
    "K.set_image_data_format('channels_last')\n",
    "from keras.utils import to_categorical\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras import callbacks\n",
    "from keras.models import Model\n",
    "\n",
    "from utils import combine_images\n",
    "from utils import plot_log\n",
    "\n",
    "from capsulelayers import CapsuleLayer, PrimaryCap, Length, Mask\n",
    "sys.path.append('./PatchyTools/')\n",
    "from PatchyConverter import PatchyConverter\n",
    "from ConvNetPatchy import ConvNetPatchy\n",
    "from DropboxLoader import DropboxLoader\n",
    "from CapsuleParameters import CapsuleParameters\n",
    "from CapsuleParameters import CapsuleTrainingParameters\n",
    "from GraphClassifier import GraphClassifier\n",
    "from keras import backend as K\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Getting the data:\n",
    "dataset_name = 'MUTAG'\n",
    "receptive_field = 10\n",
    "relabeling = True\n",
    "stride = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "importing graph data\n",
      "number of graphs in MUTAG dataset : 188\n",
      "number of features : 7\n",
      "Separating Graphs per graph ID\n",
      "getting width\n",
      "width: 18\n"
     ]
    }
   ],
   "source": [
    "# Adjeceny dict and \n",
    "data_graph = PatchyConverter(dataset_name, receptive_field,stride)\n",
    "# Converting Graphs into Matrices:\n",
    "if relabeling:\n",
    "    data_graph.relabel_graphs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MUTAG tensor exists, loading it from Dropbox\n",
      "Loading path: /Users/marcelogutierrez/.gamma_link/Samples/MUTAG/MUTAG_patchy_bc_tensor_w_18_relabelled.npy\n"
     ]
    }
   ],
   "source": [
    "graph_tensor = data_graph.graph_to_patchy_tensor_bc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MUTAG tensor exists, loading it from Dropbox\n",
      "Loading path: /Users/marcelogutierrez/.gamma_link/Samples/MUTAG/MUTAG_patchy_bc_tensor_w_18_relabelled.npy\n"
     ]
    }
   ],
   "source": [
    "nauty_tensor = data_graph.graphs_to_patchy_tensor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dropbox_loader = DropboxLoader(dataset_name)\n",
    "graph_labels = dropbox_loader.get_graph_label()\n",
    "graph_labels = np.array(graph_labels.graph_label)\n",
    "n_class = len(np.unique(graph_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Capsule Architecture Parameters:\n",
    "capsule_params= CapsuleParameters()\n",
    "\n",
    "# First conv layer: 'filters', kernel_size)\n",
    "conv_layer_name = 'conv_layer'\n",
    "conv_layer_params = {}\n",
    "conv_layer_params['filters'] = 256\n",
    "conv_layer_params['kernel_size'] = 9\n",
    "conv_layer_params['strides'] = [1, 1]\n",
    "conv_layer_params['padding'] = 'VALID'\n",
    "conv_layer_params['activation'] = 'relu'\n",
    "conv_layer_params['name'] = 'conv1'\n",
    "\n",
    "capsule_params.add_params(conv_layer_params,conv_layer_name)\n",
    "\n",
    "# First Capsule Layer:\n",
    "# [num_output_caps, caps_len,'filters',kernel_size,strides,padding]\n",
    "caps_layer_name = 'caps_layer'\n",
    "caps_layer_params = {}\n",
    "caps_layer_params['filters'] = 256\n",
    "caps_layer_params['kernel_size'] = 2\n",
    "caps_layer_params['strides'] = [2,2]\n",
    "caps_layer_params['padding'] = 'VALID'\n",
    "caps_layer_params['padding'] = 'VALID'\n",
    "caps_layer_params['n_channels'] = 32\n",
    "caps_layer_params['dim_capsule'] = 8\n",
    "caps_layer_params['name'] = 'caps_layer'\n",
    "capsule_params.add_params(caps_layer_params,caps_layer_name)\n",
    "\n",
    "# Digit Capsule Layer:\n",
    "digit_layer_name = 'digitcaps_layer'\n",
    "digit_layer_params = {}\n",
    "digit_layer_params['n_channels'] = 10\n",
    "digit_layer_params['dim_capsule'] = 16\n",
    "digit_layer_params['name'] = 'digitcaps'\n",
    "capsule_params.add_params(digit_layer_params,digit_layer_name )\n",
    "\n",
    "# Capsule Decoder:\n",
    "decoder_layer = 'decoder_layer'\n",
    "decoder_params = {}\n",
    "decoder_params['first_dense'] = 256 #250 #512\n",
    "decoder_params['second_dense'] = 512\n",
    "decoder_params['name'] = 'decoder'\n",
    "capsule_params.add_params(decoder_params,decoder_layer)\n",
    "\n",
    "# Training parameters:\n",
    "parameter_set = CapsuleTrainingParameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/marcelogutierrez/Projects/Gamma/capsuleSans/PatchyCapsules/capsulelayers.py:150: calling softmax (from tensorflow.python.ops.nn_ops) with dim is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "dim is deprecated, use axis instead\n",
      "time to generate the model: 0.7657179832458496\n",
      "Trained model saved to './result/trained_model.h5'\n",
      "validation accuracy: 0.8947368264198303\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py:4405: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "# Training Graph Classifier:\n",
    "x_train, x_test, y_train, y_test = train_test_split(graph_tensor,graph_labels,test_size=0.10,random_state=0)\n",
    "data = ((x_train, y_train), (x_test, y_test))\n",
    "input_shape = x_train.shape[1:]\n",
    "\n",
    "patchy_classifier = GraphClassifier(input_shape,n_class)\n",
    "patchy_classifier.build_the_graph(capsule_params)\n",
    "##\n",
    "#patchy_classifier.train_model.summary()\n",
    "\n",
    "parameter_set.epochs = 200\n",
    "patchy_classifier.train(data, parameter_set)\n",
    "print('validation accuracy: {}'.format(patchy_classifier.results.val_capsnet_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(160, 8)\n",
      "(188, 1280)\n"
     ]
    }
   ],
   "source": [
    "embedding_func = K.function([patchy_classifier.eval_model.input], [patchy_classifier.primarycaps])\n",
    "x_eval = graph_tensor\n",
    "y_eval = graph_labels\n",
    "node_embeddings=embedding_func([x_eval])[0]\n",
    "print(node_embeddings[0].shape)\n",
    "X = node_embeddings.reshape(node_embeddings.shape[0],-1)\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(patchy_classifier.log_file)  # ,index_col=0)\n",
    "#df = df.loc[:, ['epoch', 'capsnet_acc', 'val_capsnet_acc']]\n",
    "#results = df.iloc[-1, :]  # .val_capsnet_acc\n",
    "#df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAGECAYAAAA7lVplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 576x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation accuracy: 0.8947368264198303\n"
     ]
    }
   ],
   "source": [
    "# Multiplots\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt.plot(df.capsnet_loss)\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.title('Training Loss')\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.plot(df.capsnet_acc)\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.title('Training Accuracy')\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "plt.plot(df.val_capsnet_loss)\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.title('Validation Loss')\n",
    "\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "plt.plot(df.val_capsnet_acc)\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.title('Validation Accuracy')\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "plt.show()\n",
    "print('validation accuracy: {}'.format(patchy_classifier.results.val_capsnet_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "perplexity = 20\n",
    "embedding = TSNE(n_components=2,perplexity=perplexity,random_state = 0)\n",
    "X_r = embedding.fit_transform(X)\n",
    "target_ids = np.unique(graph_labels)\n",
    "target_names = ['neg', 'pos']\n",
    "\n",
    "embedding = TSNE(n_components=2,perplexity=perplexity,random_state = 0)\n",
    "X_patchy = graph_tensor.reshape(graph_tensor.shape[0],-1)\n",
    "X_rp = embedding.fit_transform(X_patchy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Getting the embeddings:\n",
    "#embedding_func = K.function([patchy_classifier.eval_model.input], [patchy_classifier.conv1])\n",
    "#node_embeddings = embedding_func([x_train])\n",
    "\n",
    "# embedding_func = K.function([patchy_classifier.eval_model.input], [patchy_classifier.graphcaps])\n",
    "# node_embeddings = embedding_func([x_train])[0]\n",
    "\n",
    "\n",
    "# embedding_func = K.function([patchy_classifier.eval_model.input], [patchy_classifier.primarycaps])\n",
    "# x_eval = graph_tensor\n",
    "# y_eval = graph_labels\n",
    "# node_embeddings=embedding_func([x_eval])[0]\n",
    "# print(node_embeddings[0].shape)\n",
    "# X = node_embeddings.reshape(node_embeddings.shape[0],-1)\n",
    "# print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 0 completed with val acc : 0.7894737124443054\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:9: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"co..., outputs=Tensor(\"de...)`\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "j = 0\n",
    "epochs = 200\n",
    "patchy_cnn = ConvNetPatchy(graph_tensor, graph_labels)\n",
    "patchy_cnn.split_data(random_state=j)\n",
    "patchy_cnn.build_graph()\n",
    "patchy_cnn.train_model(epochs=epochs)\n",
    "print('Fold {} completed with val acc : {}'.format(j, patchy_cnn.final_val_acc))\n",
    "\n",
    "intermediate_layer = Model(input=patchy_cnn.model.input, output= patchy_cnn.model.get_layer(index=3).output)\n",
    "X_cnn = intermediate_layer.predict(graph_tensor)\n",
    "X_cnn = X_cnn.reshape(X_cnn.shape[0],-1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "embedding = TSNE(n_components=2,perplexity=perplexity,random_state = 0)\n",
    "X_cnn_emb = embedding.fit_transform(X_cnn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "embedding = TSNE(n_components=2,perplexity=perplexity, random_state = 0)\n",
    "X_nauty = embedding.fit_transform(nauty_tensor.reshape(nauty_tensor.shape[0],-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 't-sne from patchy representations')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAFlCAYAAADGV7BOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, label in zip(target_ids, target_names):\n",
    "    plt.scatter(X_nauty[y_eval== i][:,0],X_nauty[y_eval== i][:,1], label=label)\n",
    "\n",
    "plt.xlabel('1st t-SNE Dimension')\n",
    "plt.ylabel('2nd t-SNE Dimension')\n",
    "plt.legend()\n",
    "plt.title('t-sne from patchy representations')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 't-sne from patchy representations')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7EAAALGCAYAAACTcxaUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFgAAAAAQNP4f3TO4I8P9f40AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "# Create 2x2 sub plots\n",
    "gs = gridspec.GridSpec(2, 2)\n",
    "\n",
    "plt.figure(figsize=(16,12))\n",
    "ax = plt.subplot(gs[0, 0]) # row 0, col 0\n",
    "#ax.set_axis_off()\n",
    "for i, label in zip(target_ids, target_names):\n",
    "    plt.scatter(X_rp[y_eval== i][:,0],X_rp[y_eval== i][:,1], label=label)\n",
    "\n",
    "plt.xlabel('1st t-SNE Dimension')\n",
    "plt.ylabel('2nd t-SNE Dimension')\n",
    "plt.legend()\n",
    "plt.title('t-sne from patchy representations')\n",
    "\n",
    "\n",
    "ax = plt.subplot(gs[0, 1]) # row 1, span all columns\n",
    "for i, label in zip(target_ids, target_names):\n",
    "    plt.scatter(X_cnn_emb[y_eval== i][:,0],X_cnn_emb[y_eval== i][:,1], label=label)\n",
    "\n",
    "plt.xlabel('1st t-SNE Dimension')\n",
    "plt.ylabel('2nd t-SNE Dimension')\n",
    "plt.legend()\n",
    "plt.title('t-sne from convolutional layer')\n",
    "\n",
    "ax = plt.subplot(gs[1, :]) # row 0, col 1\n",
    "for i, label in zip(target_ids, target_names):\n",
    "    plt.scatter(X_r[y_eval== i][:,0],X_r[y_eval== i][:,1], label=label)\n",
    "plt.xlabel('1st t-SNE Dimension')\n",
    "plt.ylabel('2nd t-SNE Dimension')\n",
    "plt.legend()\n",
    "plt.title('t-sne from patchy representations')\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "DIR_PATH = os.environ['GAMMA_DATA_ROOT']\n",
    "for X_to_df,model_name in zip([X_r, X_rp,X_cnn_emb],['caps','patchy','cnn']):\n",
    "    folder_name = '{}_{}_tsne.csv'.format(dataset_name,model_name)\n",
    "    path_name = os.path.join(DIR_PATH+'/Results/CapsuleSans/TSNE',folder_name)\n",
    "    df = pd.DataFrame(np.c_[X_to_df, y_eval], columns=['comp_1','comp_2','label'])\n",
    "    df.to_csv(path_name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# # Training Graph Classifier:\n",
    "# x_train, x_test, y_train, y_test = train_test_split(graph_tensor,graph_labels,test_size=0.10,random_state=0)\n",
    "# data = ((x_train, y_train), (x_test, y_test))\n",
    "# input_shape = x_train.shape[1:]\n",
    "\n",
    "# patchy_classifier = GraphClassifier(input_shape,n_class)\n",
    "# patchy_classifier.build_the_graph(capsule_params)\n",
    "# ##\n",
    "# #patchy_classifier.train_model.summary()\n",
    "\n",
    "# parameter_set.epochs = 200\n",
    "# patchy_classifier.train(data, parameter_set)\n",
    "# print('validation accuracy: {}'.format(patchy_classifier.results.val_capsnet_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Intra-cluster distance \n",
    "mse_caps = 0\n",
    "mean_patchy =0\n",
    "mse_cnn = 0\n",
    "for i in target_ids:\n",
    "    mean_point = np.tile(X_r[y_eval== i].mean(0),(X_r[y_eval== i].shape[0],1))\n",
    "    mse_caps += mean_squared_error(X_r[y_eval== i],mean_point)\n",
    "print(mse_caps)\n",
    "\n",
    "for i in target_ids:\n",
    "    mean_point = np.tile(X_rp[y_eval== i].mean(0),(X_rp[y_eval== i].shape[0],1))\n",
    "    mse_patchy += mean_squared_error(X_rp[y_eval== i],mean_point)\n",
    "print(mse_patchy)\n",
    "\n",
    "for i in target_ids:\n",
    "    mean_point = np.tile(X_cnn_emb[y_eval== i].mean(0),(X_cnn_emb[y_eval== i].shape[0],1))\n",
    "    mse_cnn += mean_squared_error(X_cnn_emb[y_eval== i],mean_point)\n",
    "print(mse_cnn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Inter-cluster distance\n",
    "mean_point1 = X_r[y_eval== target_ids[0]].mean(0)\n",
    "mean_point2 = X_r[y_eval== target_ids[1]].mean(0)\n",
    "dist_caps = mean_squared_error(mean_point1,mean_point2)\n",
    "print(dist_caps)\n",
    "\n",
    "mean_point1 = X_rp[y_eval== target_ids[0]].mean(0)\n",
    "mean_point2 = X_rp[y_eval== target_ids[1]].mean(0)\n",
    "dist_patchy = mean_squared_error(mean_point1,mean_point2)\n",
    "print(dist_patchy)\n",
    "mean_point1 = X_cnn_emb[y_eval== target_ids[0]].mean(0)\n",
    "mean_point2 = X_cnn_emb[y_eval== target_ids[1]].mean(0)\n",
    "dist_cnn = mean_squared_error(mean_point1,mean_point2)\n",
    "print(dist_cnn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mean_point = np.tile(X_cnn_emb[y_eval== i].mean(0),(X_cnn_emb[y_eval== i].shape[0],1))\n",
    "mean_squared_error(X_cnn_emb[y_eval== i],mean_point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# PCA:\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(X)\n",
    "\n",
    "\n",
    "print('explained variance ratio: {}'.format(pca.explained_variance_ratio_))\n",
    "print('Singular values : {}'.format(pca.singular_values_)  )\n",
    "target_ids = np.unique(graph_labels)\n",
    "target_names = ['neg', 'pos']\n",
    "plt.figure(figsize=(6, 5))\n",
    "for i, label in zip(target_ids, target_names):\n",
    "    plt.scatter(X_r[y_eval== i][:,0],X_r[y_eval== i][:,1], label=label)\n",
    "    plt.xlabel('1st PCA Dimension')\n",
    "    plt.ylabel('2nd PCA Dimension')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "# Create 2x2 sub plots\n",
    "gs = gridspec.GridSpec(2, 2)\n",
    "\n",
    "plt.figure(figsize=(16,12))\n",
    "ax = plt.subplot(gs[0, 0]) # row 0, col 0\n",
    "#ax.set_axis_off()\n",
    "for i, label in zip(target_ids, target_names):\n",
    "    plt.scatter(X_rp[y_eval== i][:,0],X_rp[y_eval== i][:,1], label=label)\n",
    "\n",
    "plt.xlabel('1st t-SNE Dimension')\n",
    "plt.ylabel('2nd t-SNE Dimension')\n",
    "plt.legend()\n",
    "plt.title('t-sne from patchy representations')\n",
    "\n",
    "\n",
    "ax = plt.subplot(gs[0, 1]) # row 1, span all columns\n",
    "for i, label in zip(target_ids, target_names):\n",
    "    plt.scatter(X_cnn_emb[y_eval== i][:,0],X_cnn_emb[y_eval== i][:,1], label=label)\n",
    "\n",
    "plt.xlabel('1st t-SNE Dimension')\n",
    "plt.ylabel('2nd t-SNE Dimension')\n",
    "plt.legend()\n",
    "plt.title('t-sne from convolutional layer')\n",
    "\n",
    "ax = plt.subplot(gs[1, :]) # row 0, col 1\n",
    "for i, label in zip(target_ids, target_names):\n",
    "    plt.scatter(X_r[y_eval== i][:,0],X_r[y_eval== i][:,1], label=label)\n",
    "plt.xlabel('1st t-SNE Dimension')\n",
    "plt.ylabel('2nd t-SNE Dimension')\n",
    "plt.legend()\n",
    "plt.title('t-sne from patchy representations')\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing with SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# acc_list = []\n",
    "# for j in range(10):\n",
    "#     x_train, x_test, y_train, y_test = train_test_split(graph_tensor,graph_labels,test_size=0.10,random_state=j)\n",
    "#     #data = ((x_train, y_train), (x_test, y_test))\n",
    "#     #input_shape = x_train.shape[1:]\n",
    "#     X = x_train.reshape(x_train.shape[0],-1)\n",
    "#     y = y_train.reshape(y_train.shape[0],-1)\n",
    "#     clf = svm.SVC()\n",
    "#     clf.fit(X, y)  \n",
    "#     X_test_svm = x_test.reshape(x_test.shape[0],-1)\n",
    "#     y_test_svm = y_test.reshape(y_test.shape[0],-1)\n",
    "#     y_pred = clf.predict(X_test_svm)\n",
    "#     acc = accuracy_score(y_pred,y_test_svm)\n",
    "#     acc_list.append(acc)\n",
    "#     print(acc)\n",
    "# print('mean acc : {}'.format(np.mean(acc_list)))\n",
    "# print('std acc : {}'.format(np.std(acc_list)))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# patchy_classifier = GraphClassifier(input_shape,6)\n",
    "# patchy_classifier.build_the_graph(capsule_params)\n",
    "# patchy_classifier.train(data, parameter_set)\n",
    "#patchy_classifier.train_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
